Following previous works [1]}, [2]}, we adopt two widely used metrics for evaluating top-N ranking models, Recall@K and NDCG@K. In this paper, K is set to 10 and 50. For dataset splitting, we apply the leave-one-out strategy, i.e., the latest interacted item as test data, the item before the last one as validation data, and the remaining interactions for training. The ground-truth item of each sequence is ranked among all the other items while evaluating. We finally report the average scores of all test users.
